h = glmhess(net, x, t)
h = glmhess(net, x, t) takes a GLM network data structure net,
a matrix x of input values, and a matrix t of target
values and returns the full Hessian matrix h corresponding to
the second derivatives of the negative log posterior distribution,
evaluated for the current weight and bias values as defined by
net. Note that the target data is not required in the calculation,
but is included to make the interface uniform with nethess. For
linear and logistic outputs, the computation is very simple and is
done (in effect) in one line in glmtrain.
glmtrain to take a Newton step for
softmax outputs.
Hessian = glmhess(net, x, t); deltaw = -gradient*pinv(Hessian);
glm, glmtrain, hesschek, nethessCopyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)